Merge main: dual model dropdowns + richer job logs, adapted for agent-mode.

- llm.py: set_model_overrides(vision, text) replaces the single job override;
  UI picks still beat per-call agent model args, but never name the hybrid
  local model (avoids main's hybrid footgun); local->cloud fallback uses the
  text pick.
- jobs.py: timestamped line-split tee (job_log.py), in-memory log + log_tail
  polls, full log on terminal states (done/error/needs_review/finalization_error),
  log-only disk recovery, error email links to the run log, and failed runs now
  append the full traceback to job.log. Keeps pipeline_mode, job.json, and the
  review gate.
- models.py: vision/text split via architecture modalities, pricing kept;
  /models returns {vision, text, defaults}; /check takes vision_model/text_model
  (replacing model); /health adds text_model. models_catalog.py dropped.
- UI: two priced dropdowns (OpenRouter compute only) + live run-log panel.
- Tests updated for dual overrides and the /models shape; new coverage for
  traceback capture and local-model immunity.

NOTES.md

@@ -0,0 +1,160 @@
+# Session Notes — Conflict Checker
+
+Orientation for a new coding session. Setup and Docker details live in [README.md](README.md). This file tracks what the code actually does and what tends to waste time.
+
+**Last updated:** 2026-08-02 · `agent-mode` branch (merged `main` tip `bf508bf`)
+
+## What this is
+
+Cross-discipline **design contradiction / senior-architect QAQC** for construction drawing PDFs (Arch, Struct, Mech, Elec, Plumb, FP, etc.). Flags disagreements *between disciplines* before a set goes to bid/permit.
+
+**Not** IronBid’s scope-ownership conflict checker.
+
+Source of truth: Scout IT Gitea — `gitea.scoutitsystems.com/woogi/Conflict_Checker`.
+
+## Stack
+
+| Layer | Detail |
+|-------|--------|
+| API | Python 3.12, FastAPI, Uvicorn ([backend/main.py](backend/main.py)) |
+| UI | Single static file [frontend/index.html](frontend/index.html), served by FastAPI |
+| Pipelines | **Classic:** [backend/pipeline/runner.py](backend/pipeline/runner.py) (shared by web + CLI). **Agent:** [backend/agents/runner.py](backend/agents/runner.py) — experimental scoped specialist agents, selected per job (`pipeline_mode`) |
+| Review gate | Agent jobs stop at `needs_review` for human decisions before the report emails ([backend/review/](backend/review/)) |
+| LLM | OpenRouter via `openai` SDK; default `google/gemini-2.5-pro`. Vision always OpenRouter; text stages can use local vLLM (classic/hybrid only) |
+| PDF | `pdf2image` + system `poppler-utils` → JPEG page images |
+| Jobs | In-memory threads ([backend/jobs.py](backend/jobs.py)) — no Redis/DB |
+| Tests | `pytest tests/` (~82 tests; see Quick start) |
+| Deploy | Docker Compose; app on port **8099**; public URL `https://conchecker.scoutitsystems.com` |
+
+## Live pipeline (authoritative)
+
+README still describes an older 5-stage extract-then-compare loop. **Trust `runner.py`.** Actual flow:
+
+```
+PDF → images → extract → sheet index → jurisdiction
+    → normalize → project intelligence (GOIDs)
+    → cluster → conflict reason
+    → QAQC / code / constructability
+    → dedup-validate → risk → RFIs → report
+```
+
+| Runner stage | Module | Notes |
+|--------------|--------|--------|
+| PDF → images | `pdf_processor` | Rasterize |
+| Extract assertions | `extractor` | Vision, per sheet |
+| Classify sheet index | `sheet_index` | LLM |
+| Jurisdiction profile | `jurisdiction` | After cover meta |
+| Normalize | `normalizer` | LLM batches |
+| Project intelligence | `normalizer.build_project_intelligence` | GOIDs + relationships |
+| Cluster | `llm_clusterer` or `clusterer` | Default `CLUSTERER=llm` |
+| Conflicts | `conflict_checker` | Per-cluster vision reason |
+| QAQC / code / constructability | `qaqc_review`, `code_review`, `constructability` | Full-set / batched |
+| Validate & dedup | `validator` | Merges conflict + QAQC + code + construct issues |
+| Risk / RFIs | `risk`, `rfi` | Text-only |
+| Report | `report` | `conflicts.json` + `report.md` (+ stage JSON dumps when `out_dir` set) |
+
+Design notes for Stage 2/3 engines also live under `Changes/*.docx`.
+
+**Agent pipeline** (`pipeline_mode=agent`): scoped specialist agents in [backend/agents/](backend/agents/) (extractors, linker, brain, critics, RFI writer) run through `Orchestrator` + `ProjectMemory`; artifacts under `outputs/<job_id>/agent/`. Agent mode is OpenRouter-only (no hybrid) and, when `AGENT_REQUIRE_REVIEW=true`, stops at `needs_review` until a human saves decisions and finalizes via the review endpoints. Design docs: `docs/superpowers/`.
+
+## HTTP API (current)
+
+| Method | Path | Purpose |
+|--------|------|---------|
+| GET | `/health` | Liveness + `model` / `text_model` + `version` / `build` + key/email flags |
+| GET | `/models` | OpenRouter catalog split into `vision[]` / `text[]` + `defaults`, with per-1M-token pricing (cached ~1h; **502** when OpenRouter is unreachable) |
+| POST | `/check` | Upload PDF; returns `{job_id}` immediately |
+| GET | `/jobs/{id}` | Status poll. Running: `stage` + `log_tail`. Done/error/needs_review/finalization_error: `report` and/or `error` + full `log` |
+| GET | `/jobs/{id}/log` | Full run log as `text/plain` (404 when no log file) |
+| GET | `/jobs/{id}/review` | Review queue + progress + saved decisions (agent jobs) |
+| POST | `/jobs/{id}/review-decisions` | Save reviewer decisions (409 outside needs_review/reviewing) |
+| POST | `/jobs/{id}/finalize-review` | Background finalize + send report (409 unless review gate passed) |
+| GET | `/jobs/{id}/sheet-image/{page}` | JPEG of source PDF page for the sheet viewer |
+| GET | `/` | Serves `frontend/index.html` |
+
+### `/check` form fields
+
+- Required: `file` (PDF)
+- Optional: `notification_email`, `project_name`, `address`, `occupancy`, `work_type`
+- Pipeline: `pipeline_mode` (`classic` default, or `agent`)
+- Compute: `text_local` (`true` = hybrid local text; forced off for agent mode)
+- Models: `vision_model`, `text_model` (OpenRouter ids; blank = config defaults)
+
+## Job logs
+
+Pipeline `print()` is teed for the job thread ([backend/job_log.py](backend/job_log.py)):
+
+- Live: `GET /jobs/{id}` → `log_tail` (last 80 lines)
+- Done/error/needs_review/finalization_error: same payload includes full `log`
+- Disk: `backend/outputs/<job_id>/job.log` (survives restart; status registry does not)
+- API: `GET /jobs/{id}/log` → `text/plain`
+- UI: “Run log” panel updates while running; stays visible after finish/fail/review
+- Failures append the **full traceback** to the log; each run starts with a header line (job id, mode, models, start time)
+- `outputs/<job_id>/job.json` (written at start) carries email/mode/models so the disk fallback can rebuild a job after restart
+
+## Vision vs text models
+
+Two models, not one:
+
+| Role | Config | Stages | Backend |
+|------|--------|--------|---------|
+| Vision | `MODEL` | Extract, conflict reason (images) | Always OpenRouter |
+| Text | `TEXT_MODEL` (falls back to `MODEL`) | Sheet index, jurisdiction, normalize, cluster(LLM), QAQC, code, construct, validate, risk, RFI | OpenRouter, or local when hybrid |
+
+UI: two dropdowns (with per-1M pricing) filled from `GET /models` ([backend/models.py](backend/models.py)), shown only for OpenRouter compute. Per-run picks go through `set_model_overrides(vision, text)` in [backend/llm.py](backend/llm.py): classic runs pass them as `run_pipeline` kwargs (runner clears in `finally`); agent runs set them module-level around `run_agent_pipeline`. UI picks beat per-call agent `AGENT_*_MODEL` args but **never name the hybrid local model** — local stays on `LOCAL_TEXT_MODEL`; the text pick only covers the cloud fallback.
+
+## Where to change what
+
+| Concern | File |
+|---------|------|
+| Prompts, vocab, conflict taxonomy | [backend/prompts.py](backend/prompts.py) |
+| Env knobs | [backend/config.py](backend/config.py), [backend/.env.example](backend/.env.example) |
+| HTTP API | [backend/main.py](backend/main.py) |
+| UI (upload, models, live log, results) | [frontend/index.html](frontend/index.html) |
+| CLI tuning loop | [cli/run_check.py](cli/run_check.py) |
+| LLM client, cache, cost, model overrides | [backend/llm.py](backend/llm.py) |
+| OpenRouter catalog + pricing + vision/text split | [backend/models.py](backend/models.py) |
+| Agent-mode pipeline | [backend/agents/](backend/agents/) (`runner.py` entry; agents call `llm.call_json` with per-call model args) |
+| Human review gate (queue, decisions, finalize) | [backend/review/](backend/review/) |
+| Job registry + stdout tee log | [backend/jobs.py](backend/jobs.py), [backend/job_log.py](backend/job_log.py) |
+| Stage helpers (prompt render, issue validate) | [backend/pipeline/_stage.py](backend/pipeline/_stage.py) |
+| Code text corpus (Stage 7) | [backend/code_corpus/](backend/code_corpus/) |
+| Hybrid local LLM helper | [scripts/setup_vllm.sh](scripts/setup_vllm.sh) |
+
+Older prompt snapshot: `backend/prompts.py.v1`.
+
+## Gotchas
+
+1. **Prompt placeholders** — Use `str.replace` via `_stage.render`, never `str.format`. Prompts contain literal `{` JSON braces.
+2. **Clustering** — Default is LLM (`CLUSTERER=llm`); empty LLM result falls back to deterministic. Deterministic clusters need ≥2 disciplines (or schedule-vs-plan); single-discipline “missing” gaps are a known limit.
+3. **Jobs are in-memory** — Process restart clears job status; `outputs/<job_id>/` (report + `job.log` + `job.json` + `source.pdf`) still reload via disk fallback, including `needs_review` recovery.
+4. **Dependency pin** — `httpx==0.27.2` with `openai==1.51.0`. httpx ≥0.28 breaks openai’s `proxies=` kwarg.
+5. **Code corpus licensing** — Only `ada_2010.txt` is shipped. Do not paste IBC/IFC/IECC without a license (see `backend/code_corpus/README.md`).
+6. **Dual assertion schema** — Newer `{sheet, objects[]}` is mapped to legacy `{assertions[]}` with `attribute`/`value` for older stages.
+7. **Grounding guard** — Extractor drops objects whose numeric claims are not in `source_text` (graphical-only objects allowed).
+8. **Module globals** — LLM cost counters, stdout tee, and model overrides are process-global; overlapping jobs can interleave (single-user tool assumption).
+9. **Samples** — `samples/*.pdf` are gitignored; drop PDFs locally for CLI runs.
+10. **README drift** — Treat README for setup/CI; treat this file + `runner.py` for pipeline truth. `prompts.py` header may still say some prompts are unwired — they are wired through the runner.
+11. **Git identity** — This box has no `user.name` / `user.email`; commits need `GIT_AUTHOR_*` / `GIT_COMMITTER_*` env vars (do not `git config`). Remote push to Gitea works.
+12. **No local Python deps on host** — App is meant to run in Docker; bare `python3` imports may miss `dotenv` / `httpx`. Prefer `docker compose`. For tests on this box: venv + requirements, but unpin Pillow (`Pillow>=11`) — 10.4.0 doesn't build on Python 3.14 (Docker uses 3.12, where the pin is fine).
+13. **Agent mode constraints** — OpenRouter-only (hybrid disabled in UI and forced off server-side); review gate statuses are `needs_review → reviewing → finalizing → done` (`finalization_error` on finalize failure); only terminal states include the full `log` in polls.
+
+## Quick start pointers
+
+- Full setup: [README.md](README.md) (`docker compose up -d --build` → http://localhost:8099).
+- Local CLI: `python cli/run_check.py samples/your_set.pdf --out out/your_set`.
+- Prompt iteration: set `LLM_CACHE=true` in `backend/.env` so unchanged stages replay for free; clear with `rm -rf backend/.llm_cache`.
+- Artifacts per job: `assertions.json`, `clusters.json`, per-stage JSON, `conflicts.json`, `report.md`, `job.log`, `job.json`, `source.pdf` under `backend/outputs/<job_id>/`.
+- Tests: `python -m pytest tests/` (needs the deps from `requirements.txt` + `pytest`; on this box use a venv, see gotcha #12).
+
+## Recent work (2026-08-02, agent-mode)
+
+Merged `main` tip (`a6b0c8f` + `bf508bf`) into `agent-mode`, reconciling with this branch's own earlier implementations:
+
+- **Two model dropdowns** — main's vision/text split ported onto this branch's priced catalog (`models.py`); pickers stay OpenRouter-compute-only, and UI picks never override `LOCAL_TEXT_MODEL` (main's hybrid footgun avoided).
+- **Better run logs** — main's timestamped line-splitting tee, `log_tail` polls, terminal-state full log, and log-only disk recovery merged with this branch's header line, `job.json` metadata, and review-gate states. Failed runs now also append the traceback to `job.log`.
+- `backend/models_catalog.py` (main's unpriced catalog) intentionally dropped in favor of `models.py`.
+
+## Conflict categories (taxonomy)
+
+Defined in `backend/prompts.py`: `dimensional_disagreement`, `elevation_disagreement`, `location_mismatch`, `missing_element`, `schedule_vs_plan_mismatch`, `detail_vs_plan_mismatch`, `tag_or_reference_inconsistency`, `spatial_clash`, `note_or_spec_contradiction`.

backend/job_log.py

@@ -0,0 +1,93 @@
+"""
+job_log.py - Capture pipeline stdout/stderr into a per-job log.
+
+The pipeline already prints stage progress via print(). For a reviewable
+post-run log we tee those lines into memory + outputs/<job_id>/job.log
+without rewriting every call site.
+"""
+
+import sys
+import time
+from contextlib import contextmanager
+from typing import Callable, Iterator, List, Optional, TextIO
+
+
+class _LineSplitter:
+    """Accumulate write() chunks and emit complete lines."""
+
+    def __init__(self, on_line: Callable[[str], None]):
+        self._buf = ""
+        self._on_line = on_line
+
+    def write(self, s: str) -> None:
+        if not s:
+            return
+        self._buf += s
+        while "\n" in self._buf:
+            line, self._buf = self._buf.split("\n", 1)
+            # Strip trailing CR from Windows-ish streams; keep content intact.
+            self._on_line(line.rstrip("\r"))
+
+    def flush_remainder(self) -> None:
+        if self._buf:
+            self._on_line(self._buf.rstrip("\r"))
+            self._buf = ""
+
+
+class _Tee:
+    """Mirror writes to the original stream and a line callback."""
+
+    def __init__(self, stream: TextIO, on_line: Callable[[str], None]):
+        self._stream = stream
+        self._lines = _LineSplitter(on_line)
+
+    def write(self, s: str) -> int:
+        n = self._stream.write(s)
+        self._stream.flush()
+        self._lines.write(s)
+        return n
+
+    def flush(self) -> None:
+        self._stream.flush()
+
+    def flush_remainder(self) -> None:
+        self._lines.flush_remainder()
+
+    def __getattr__(self, name: str):
+        return getattr(self._stream, name)
+
+
+def stamp_line(line: str, t: Optional[float] = None) -> str:
+    """Prefix a log line with HH:MM:SS."""
+    ts = time.strftime("%H:%M:%S", time.localtime(t if t is not None else time.time()))
+    return f"[{ts}] {line}"
+
+
+@contextmanager
+def capture_stdio(on_line: Callable[[str], None]) -> Iterator[None]:
+    """
+    Tee sys.stdout and sys.stderr into on_line(raw_line) for the duration.
+
+    Safe for the single-job-at-a-time usage of this app; overlapping jobs
+    would interleave (same limitation as the LLM cost counters).
+    """
+    old_out, old_err = sys.stdout, sys.stderr
+    tee_out = _Tee(old_out, on_line)
+    tee_err = _Tee(old_err, on_line)
+    sys.stdout = tee_out  # type: ignore[assignment]
+    sys.stderr = tee_err  # type: ignore[assignment]
+    try:
+        yield
+    finally:
+        tee_out.flush_remainder()
+        tee_err.flush_remainder()
+        sys.stdout = old_out
+        sys.stderr = old_err
+
+
+def read_log_file(path: str) -> List[str]:
+    try:
+        with open(path, encoding="utf-8") as f:
+            return [ln.rstrip("\n") for ln in f]
+    except OSError:
+        return []

backend/jobs.py

@@ -9,61 +9,33 @@ for the completion email.
 State is in-memory (fine for a single-user tool); the report is also persisted
 to outputs/<job_id>/ so results survive a restart even though live status does
 not. No external queue/DB.
+
+A teed stdout/stderr log is kept in memory and written to outputs/<job_id>/job.log
+so failed or suspicious runs can be reviewed after the fact.
 """
 
 import json
-import contextlib
 import os
-import sys
 import time
+import traceback
 import uuid
 import shutil
 import threading
-from typing import Dict, Optional
+from typing import Dict, List, Optional
 
 from backend import config
 from backend import llm
+from backend.job_log import capture_stdio, read_log_file, stamp_line
 from backend.agents.runner import run_agent_pipeline
 from backend.pipeline.runner import run_pipeline
 from backend.email_sender import send_conflict_report, send_review_required
 
 _jobs: Dict[str, Dict] = {}
 _lock = threading.Lock()
+_LOG_TAIL = 80
 PIPELINE_MODES = {"classic", "agent"}
-
+# States where the job will produce no more log output; polls get the full log.
-
+_TERMINAL_STATES = {"done", "error", "needs_review", "finalization_error"}
-class _Tee:
-    """Write to both the real stream and the job log file."""
-
-    def __init__(self, stream, log_file) -> None:
-        self._stream = stream
-        self._log = log_file
-
-    def write(self, data):
-        self._stream.write(data)
-        self._log.write(data)
-
-    def flush(self):
-        self._stream.flush()
-        self._log.flush()
-
-
-@contextlib.contextmanager
-def _tee_log(log_path: str, header: str):
-    """Mirror stdout/stderr into a per-job log file for the duration of a run.
-
-    sys.stdout is process-global, so two concurrent jobs would interleave in
-    each other's logs - acceptable for this single-user tool (same tradeoff as
-    the LLM cost globals in llm.py).
-    """
-    with open(log_path, "a", encoding="utf-8") as log_file:
-        log_file.write(header + "\n")
-        real_out, real_err = sys.stdout, sys.stderr
-        sys.stdout, sys.stderr = _Tee(real_out, log_file), _Tee(real_err, log_file)
-        try:
-            yield
-        finally:
-            sys.stdout, sys.stderr = real_out, real_err
 
 
 def _set(job_id: str, **fields) -> None:
@@ -71,16 +43,39 @@ def _set(job_id: str, **fields) -> None:
         _jobs[job_id].update(fields)
 
 
-def create_job(pdf_path: str, source_filename: str, email: Optional[str] = None,
+def _append_log(job_id: str, raw_line: str, log_path: str) -> None:
-               project_input: Optional[Dict] = None, text_local: bool = False,
+    """Stamp, store, and append one captured stdout/stderr line."""
-               pipeline_mode: str = "classic", model: Optional[str] = None) -> str:
+    entry = stamp_line(raw_line)
+    with _lock:
+        job = _jobs.get(job_id)
+        if job is not None:
+            job.setdefault("log", []).append(entry)
+    try:
+        os.makedirs(os.path.dirname(log_path), exist_ok=True)
+        with open(log_path, "a", encoding="utf-8") as f:
+            f.write(entry + "\n")
+    except OSError:
+        # Don't fail the job over log I/O; avoid print() here — it would
+        # re-enter the stdio tee while a job is capturing.
+        pass
+
+
+def create_job(
+    pdf_path: str,
+    source_filename: str,
+    email: Optional[str] = None,
+    project_input: Optional[Dict] = None,
+    text_local: bool = False,
+    pipeline_mode: str = "classic",
+    vision_model: Optional[str] = None,
+    text_model: Optional[str] = None,
+) -> str:
     """Register a job and kick off its background thread. Returns the job_id."""
     pipeline_mode = pipeline_mode.strip().lower()
     if pipeline_mode not in PIPELINE_MODES:
         raise ValueError(f"Unsupported pipeline mode: {pipeline_mode!r}")
     # Agent mode v1 is OpenRouter-only.
     text_local = bool(text_local and pipeline_mode == "classic")
-    model = (model or "").strip() or None
     job_id = uuid.uuid4().hex[:12]
     with _lock:
         _jobs[job_id] = {
@@ -91,35 +86,61 @@ def create_job(pdf_path: str, source_filename: str, email: Optional[str] = None,
             "project_input": project_input or {},
             "text_local": text_local,
             "pipeline_mode": pipeline_mode,
-            "model": model,
+            "vision_model": (vision_model or "").strip() or None,
+            "text_model": (text_model or "").strip() or None,
             "stage": None,
             "created_at": time.time(),
             "finished_at": None,
             "report": None,
             "error": None,
+            "log": [],
         }
-    threading.Thread(target=_run, args=(
+    threading.Thread(
-        job_id, pdf_path, project_input, text_local, pipeline_mode, model,
+        target=_run,
-    ),
+        args=(job_id, pdf_path, project_input, text_local, pipeline_mode,
-                     daemon=True).start()
+              vision_model, text_model),
+        daemon=True,
+    ).start()
     return job_id
 
 
-def _run(job_id: str, pdf_path: str, project_input: Optional[Dict] = None,
+def _run(
-         text_local: bool = False, pipeline_mode: str = "classic",
+    job_id: str,
-         model: Optional[str] = None) -> None:
+    pdf_path: str,
+    project_input: Optional[Dict] = None,
+    text_local: bool = False,
+    pipeline_mode: str = "classic",
+    vision_model: Optional[str] = None,
+    text_model: Optional[str] = None,
+) -> None:
     out_dir = os.path.join(config.OUTPUT_DIR, job_id)
+    log_path = os.path.join(out_dir, "job.log")
     try:
         _set(job_id, status="running")
-        # Keep a copy of the source PDF so its sheets can be viewed later.
         os.makedirs(out_dir, exist_ok=True)
+        # Truncate any leftover log if job_id somehow collided (shouldn't).
+        with open(log_path, "w", encoding="utf-8"):
+            pass
         header = (f"=== Job {job_id} | {pipeline_mode} | {_jobs[job_id].get('source')} | "
-                  f"model={model or 'default'} | "
+                  f"vision={vision_model or 'default'} text={text_model or 'default'} | "
                   f"started {time.strftime('%Y-%m-%d %H:%M:%S %Z', time.gmtime())} UTC ===")
-        with _tee_log(os.path.join(out_dir, "job.log"), header):
+        _append_log(job_id, header, log_path)
+
+        def on_line(raw: str) -> None:
+            _append_log(job_id, raw, log_path)
+
+        with capture_stdio(on_line):
             _run_pipeline(job_id, pdf_path, out_dir, project_input, text_local,
-                          pipeline_mode, model)
+                          pipeline_mode, vision_model, text_model)
     except Exception as e:
+        # Land the failure AND its traceback in the job log so failed runs can
+        # be diagnosed from the log alone (the stdio tee is already torn down).
+        try:
+            _append_log(job_id, f"[Jobs] Job {job_id} failed: {e}", log_path)
+            for ln in traceback.format_exc().rstrip().splitlines():
+                _append_log(job_id, ln, log_path)
+        except Exception:
+            pass
         print(f"[Jobs] Job {job_id} failed: {e}")
         _set(job_id, status="error", error=str(e), finished_at=time.time())
         _notify_error(job_id)
@@ -132,7 +153,8 @@ def _run(job_id: str, pdf_path: str, project_input: Optional[Dict] = None,
 
 def _run_pipeline(job_id: str, pdf_path: str, out_dir: str,
                   project_input: Optional[Dict], text_local: bool,
-                  pipeline_mode: str, model: Optional[str]) -> None:
+                  pipeline_mode: str, vision_model: Optional[str],
+                  text_model: Optional[str]) -> None:
     """The body of a job run; executes inside the job's tee'd log capture."""
     # Persist minimal job metadata so the disk fallback in get_job can
     # recover the recipient email / pipeline mode after a server restart
@@ -143,7 +165,10 @@ def _run_pipeline(job_id: str, pdf_path: str, out_dir: str,
             "email": _jobs[job_id].get("email"),
             "pipeline_mode": pipeline_mode,
             "source": _jobs[job_id].get("source"),
+            "vision_model": vision_model,
+            "text_model": text_model,
         }, f, indent=2)
+    # Keep a copy of the source PDF so its sheets can be viewed later.
     shutil.copy2(pdf_path, os.path.join(out_dir, "source.pdf"))
     runner = run_agent_pipeline if pipeline_mode == "agent" else run_pipeline
     runner_kwargs = {
@@ -153,17 +178,22 @@ def _run_pipeline(job_id: str, pdf_path: str, out_dir: str,
         "source_name": _jobs[job_id].get("source"),
     }
     if pipeline_mode == "classic":
+        # run_pipeline takes the picks as params and clears them in finally.
         runner_kwargs["text_local"] = text_local
+        runner_kwargs["vision_model"] = vision_model
+        runner_kwargs["text_model"] = text_model
     else:
         runner_kwargs["require_review"] = config.AGENT_REQUIRE_REVIEW
-    if model:
+        # The agent runner has no override params; set them module-level.
-        print(f"[Jobs] Model override for this run: {model}")
+        if vision_model or text_model:
-        llm.set_model_override(model)
+            print(f"[Jobs] Model overrides for this run: "
+                  f"vision={vision_model or '(default)'} text={text_model or '(default)'}")
+            llm.set_model_overrides(vision_model, text_model)
     try:
         report = runner(pdf_path, **runner_kwargs)
     finally:
-        if model:
+        if pipeline_mode == "agent":
-            llm.set_model_override(None)
+            llm.set_model_overrides(None, None)
     report.setdefault("summary", {})["pipeline_mode"] = pipeline_mode
     if report["summary"].get("agent_status") == "needs_review":
         # Human-review gate: hold the job, don't email the unreviewed report.
@@ -197,11 +227,6 @@ def _notify_error(job_id: str) -> None:
     email = job.get("email")
     if not email:
         return
-    # Reuse the report mailer with a minimal error-shaped payload.
-    err_report = {
-        "source": job.get("source", ""),
-        "summary": {"conflicts_found": 0, "by_severity": {}, "disciplines": []},
-    }
     try:
         from backend.email_sender import _smtp_ready, _send
         from email.message import EmailMessage
@@ -216,6 +241,7 @@ def _notify_error(job_id: str) -> None:
             "Your conflict check did not complete.\n\n"
             f"Drawing set: {job.get('source','')}\n"
             f"Error: {job.get('error','unknown')}\n\n"
+            f"Review the run log at: {config.APP_BASE_URL.rstrip('/')}/?job={job_id}\n\n"
             "Generated by Conflict Checker"
         )
         _send(msg)
@@ -223,28 +249,63 @@ def _notify_error(job_id: str) -> None:
         print(f"[Email] Failed to send error notice: {e}")
 
 
+def _log_from_disk(job_id: str) -> List[str]:
+    return read_log_file(os.path.join(config.OUTPUT_DIR, job_id, "job.log"))
+
+
+def get_job_log(job_id: str) -> Optional[List[str]]:
+    """Full job log lines, from memory or disk. None if job unknown."""
+    with _lock:
+        job = _jobs.get(job_id)
+        if job is not None:
+            return list(job.get("log") or [])
+
+    log = _log_from_disk(job_id)
+    # Job exists on disk if we have a log or a report artifact.
+    report_path = os.path.join(config.OUTPUT_DIR, job_id, "conflicts.json")
+    if log or os.path.isfile(report_path):
+        return log
+    return None
+
+
 def get_job(job_id: str) -> Optional[Dict]:
     """Public job view. Includes the full report only when done.
 
-    Falls back to the on-disk conflicts.json when the job isn't in the
+    Falls back to the on-disk artifacts (conflicts.json / job.log) when the
-    in-memory registry (e.g. after a server restart).
+    job isn't in the in-memory registry (e.g. after a server restart).
     """
     with _lock:
         job = _jobs.get(job_id)
         if job:
-            return dict(job)
+            out = dict(job)
+            log = list(job.get("log") or [])
+            out["log_tail"] = log[-_LOG_TAIL:]
+            # Full log on terminal states so the UI can show it without a
+            # second fetch; keep polls light while running.
+            if out.get("status") in _TERMINAL_STATES:
+                out["log"] = log
+            else:
+                out.pop("log", None)
+            return out
 
     # Try loading from disk
     report_path = os.path.join(config.OUTPUT_DIR, job_id, "conflicts.json")
-    if not os.path.isfile(report_path):
+    log = _log_from_disk(job_id)
+    if not os.path.isfile(report_path) and not log:
         return None
     try:
-        with open(report_path, encoding="utf-8") as f:
+        report = None
-            report = json.load(f)
+        if os.path.isfile(report_path):
-        summary = report.get("summary", {})
+            with open(report_path, encoding="utf-8") as f:
-        # Recover the job's real state: a job that stopped at the review gate
+                report = json.load(f)
-        # must come back as needs_review (not done) or it can never finalize.
+        summary = (report or {}).get("summary", {})
-        status = "needs_review" if summary.get("agent_status") == "needs_review" else "done"
+        if report is None:
+            # Crashed before writing a report; the log is the only artifact.
+            status = "error"
+        else:
+            # Recover the job's real state: a job that stopped at the review gate
+            # must come back as needs_review (not done) or it can never finalize.
+            status = "needs_review" if summary.get("agent_status") == "needs_review" else "done"
         # job.json (written at job start) carries the recipient email and
         # pipeline mode so the final notification still fires after a restart.
         # Missing/corrupt job.json degrades to the previous derivations.
@@ -261,16 +322,20 @@ def get_job(job_id: str) -> Optional[Dict]:
         job = {
             "job_id":       job_id,
             "status":       status,
-            "source":       meta.get("source") or report.get("source", os.path.basename(report_path)),
+            "source":       meta.get("source") or (report or {}).get("source", os.path.basename(report_path)),
             "email":        meta.get("email"),
-            "project_input": report.get("project_input", {}),
+            "project_input": (report or {}).get("project_input", {}),
             "text_local":   summary.get("text_backend") == "local",
             "pipeline_mode": meta.get("pipeline_mode") or summary.get("pipeline_mode", "classic"),
+            "vision_model": meta.get("vision_model"),
+            "text_model":   meta.get("text_model"),
             "stage":        None,
             "created_at":   os.path.getmtime(source_pdf) if os.path.isfile(source_pdf) else None,
-            "finished_at":  os.path.getmtime(report_path),
+            "finished_at":  os.path.getmtime(report_path) if os.path.isfile(report_path) else None,
             "report":       report,
-            "error":        None,
+            "error":        None if report is not None else "Report missing; see job log",
+            "log":          log,
+            "log_tail":     log[-_LOG_TAIL:],
         }
         # Hydrate the in-memory registry so _set(...) transitions (reviewing,
         # finalizing, done) work for restart-recovered jobs.

backend/llm.py

@@ -23,16 +23,13 @@ _clients: Dict[str, OpenAI] = {}
 # call_json when routing a no-image (text) call. Module-global mirrors the
 # set_stage/cost pattern (single-user tool).
 _text_local = False
-
+# Per-job model overrides (user picked models in the UI). Same module-global
-# Per-job model override (user picked a model in the UI). Same module-global
 # pattern: set by the job runner before the pipeline starts, cleared after.
-_model_override: Optional[str] = None
+# Vision applies to image calls, text to no-image calls on OpenRouter (and to
-
+# the local->cloud fallback). The LOCAL endpoint's model name is never taken
-
+# from these overrides - hybrid local keeps LOCAL_TEXT_MODEL.
-def set_model_override(model: Optional[str]) -> None:
+_vision_model_override: Optional[str] = None
-    """Override the model for all OpenRouter calls (vision + text), or None to clear."""
+_text_model_override: Optional[str] = None
-    global _model_override
-    _model_override = (model or "").strip() or None
 
 
 def set_text_backend(local: bool) -> None:
@@ -40,6 +37,13 @@ def set_text_backend(local: bool) -> None:
     global _text_local
     _text_local = bool(local)
 
+
+def set_model_overrides(vision: Optional[str] = None, text: Optional[str] = None) -> None:
+    """Per-run OpenRouter vision/text model picks. None/blank clears to defaults."""
+    global _vision_model_override, _text_model_override
+    _vision_model_override = (vision or "").strip() or None
+    _text_model_override = (text or "").strip() or None
+
 # --- per-job cost accounting -------------------------------------------------
 # OpenRouter returns the real USD cost of each call when we request usage
 # accounting. We accumulate it in a module-level counter; the runner resets it
@@ -177,17 +181,22 @@ def _resolve_backend(has_images: bool, model_override: Optional[str]) -> Dict[st
         return {
             "base_url": config.LOCAL_BASE_URL,
             "api_key": config.LOCAL_API_KEY,
+            # Local model name comes from per-call args or LOCAL_TEXT_MODEL —
+            # never the UI's OpenRouter picks, which a local server won't serve.
             "model": model_override or config.LOCAL_TEXT_MODEL or config.TEXT_MODEL,
             "usage": False,          # local has no OpenRouter usage accounting
             "local": True,
         }
     # Vision, or text-on-OpenRouter (default / fallback). A per-job override
     # (user's UI model pick) wins over per-call and env defaults.
-    default_model = config.MODEL if has_images else config.TEXT_MODEL
+    if has_images:
+        model = _vision_model_override or model_override or config.MODEL
+    else:
+        model = _text_model_override or model_override or config.TEXT_MODEL
     return {
         "base_url": config.AI_BASE_URL,
         "api_key": config.AI_API_KEY,
-        "model": _model_override or model_override or default_model,
+        "model": model,
         "usage": True,
         "local": False,
     }
@@ -362,7 +371,8 @@ def call_json(
                     _models["text_local"].add(be["model"])
                     _models["fallback_count"] += 1
                 be = {"base_url": config.AI_BASE_URL, "api_key": config.AI_API_KEY,
-                      "model": config.TEXT_MODEL, "usage": True, "local": False}
+                      "model": _text_model_override or config.TEXT_MODEL,
+                      "usage": True, "local": False}
                 cache_key = None       # don't cache fallback under the local-model key
                 fell_back = True
                 continue

backend/main.py

@@ -35,6 +35,7 @@ _FRONTEND_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "
 @app.get("/health")
 def health():
     return {"status": "ok", "model": config.MODEL,
+            "text_model": config.TEXT_MODEL,
             "version": config.APP_VERSION,
             "build": config.APP_BUILD,
             "key_configured": bool(config.AI_API_KEY),
@@ -43,13 +44,15 @@ def health():
 
 @app.get("/models")
 def list_models():
-    """Available OpenRouter models with per-1M-token pricing for the UI picker."""
+    """Vision/text OpenRouter model lists with pricing for the UI dropdowns."""
-    from backend.models import fetch_models
+    from backend.models import fetch_models, split_vision_text
     models = fetch_models()
     if models is None:
         raise HTTPException(status_code=502,
                             detail="Could not fetch the model list from OpenRouter")
-    return {"models": models, "default": config.MODEL, "default_text": config.TEXT_MODEL}
+    vision, text = split_vision_text(models)
+    return {"vision": vision, "text": text,
+            "defaults": {"vision": config.MODEL, "text": config.TEXT_MODEL}}
 
 
 @app.get("/jobs/{job_id}/log")
@@ -72,7 +75,8 @@ async def check(
     work_type: Optional[str] = Form(None),
     text_local: bool = Form(False),
     pipeline_mode: str = Form("classic"),
-    model: Optional[str] = Form(None),
+    vision_model: Optional[str] = Form(None),
+    text_model: Optional[str] = Form(None),
 ):
     """
     Accept a PDF, start a background conflict check, and return a job_id
@@ -81,6 +85,9 @@ async def check(
     Optional intake fields (project_name/address/occupancy/work_type) feed the
     Stage 0 jurisdiction profile; anything left blank is derived from the cover
     sheet.
+
+    vision_model / text_model override the configured defaults for this run
+    (vision always OpenRouter; text follows the OpenRouter vs hybrid choice).
     """
     if not file.filename.lower().endswith(".pdf"):
         raise HTTPException(status_code=400, detail="Please upload a PDF.")
@@ -106,9 +113,12 @@ async def check(
         }.items()
         if v and v.strip()
     }
+    v_model = (vision_model or "").strip() or None
+    t_model = (text_model or "").strip() or None
     job_id = create_job(tmp_path, source_filename=file.filename, email=email,
                         project_input=project_input, text_local=text_local,
-                        pipeline_mode=pipeline_mode, model=model)
+                        pipeline_mode=pipeline_mode, vision_model=v_model,
+                        text_model=t_model)
     return JSONResponse({
         "job_id": job_id,
         "status": "queued",

backend/models.py

@@ -3,11 +3,13 @@ models.py - Fetch the available OpenRouter model list with pricing (cached).
 
 The /models endpoint is public (no API key needed). Results are normalized to
 per-1M-token USD costs for display and cached in memory for an hour; callers
-degrade gracefully when OpenRouter is unreachable.
+degrade gracefully when OpenRouter is unreachable. Each entry also carries a
+vision flag (accepts image input) so the UI can offer separate vision/text
+model dropdowns.
 """
 
 import time
-from typing import List, Optional
+from typing import List, Optional, Tuple
 
 import httpx
 
@@ -25,6 +27,18 @@ def _per_mtok(rate) -> float:
         return 0.0
 
 
+def _is_vision(item: dict) -> bool:
+    """True when the model accepts image input and produces text output."""
+    arch = item.get("architecture") or {}
+    inputs = arch.get("input_modalities") or []
+    outputs = arch.get("output_modalities") or []
+    # Legacy string form: "text+image->text"
+    modality = (arch.get("modality") or "").lower()
+    has_image_in = ("image" in inputs) or ("image" in modality.split("->")[0])
+    has_text_out = ("text" in outputs) or ("->text" in modality) or (not outputs and not modality)
+    return has_image_in and has_text_out
+
+
 def _fetch_openrouter_models() -> Optional[List[dict]]:
     """Raw GET of the OpenRouter model list; None on any failure."""
     try:
@@ -55,6 +69,7 @@ def fetch_models(force: bool = False) -> Optional[List[dict]]:
             "prompt_usd_per_mtok": _per_mtok((item.get("pricing") or {}).get("prompt")),
             "completion_usd_per_mtok": _per_mtok((item.get("pricing") or {}).get("completion")),
             "context_length": item.get("context_length"),
+            "vision": _is_vision(item),
         }
         for item in data
         if item.get("id")
@@ -63,3 +78,13 @@ def fetch_models(force: bool = False) -> Optional[List[dict]]:
     _cache["models"] = models
     _cache["at"] = time.time()
     return models
+
+
+def split_vision_text(models: List[dict]) -> Tuple[List[dict], List[dict]]:
+    """Partition the normalized catalog into (vision, text) lists for the UI.
+
+    Every catalog model takes text in/out, so vision models appear in both
+    lists (same dicts, pricing included).
+    """
+    vision = [m for m in models if m.get("vision")]
+    return vision, list(models)

backend/pipeline/runner.py

@@ -42,7 +42,9 @@ from backend.pipeline.risk import score_and_prioritize
 from backend.pipeline.rfi import generate_rfis
 from backend.pipeline.report import build_report, to_markdown
 from backend.pipeline._stage import validate_issue
-from backend.llm import reset_cost, get_cost, set_stage, set_text_backend
+from backend.llm import (
+    reset_cost, get_cost, set_stage, set_text_backend, set_model_overrides,
+)
 
 
 def run_pipeline(
@@ -52,6 +54,8 @@ def run_pipeline(
     project_input: Optional[Dict] = None,
     source_name: Optional[str] = None,
     text_local: bool = False,
+    vision_model: Optional[str] = None,
+    text_model: Optional[str] = None,
 ) -> Dict:
     """
     Run the full QAQC pipeline on one PDF and return the report dict.
@@ -59,6 +63,9 @@ def run_pipeline(
     project_input: optional intake fields (project_name, address, occupancy,
     work_type). Cover-sheet-derived values fill any gaps; intake fields win.
 
+    vision_model / text_model: optional per-run OpenRouter (or local text)
+    model overrides from the UI. Blank/None keeps config defaults.
+
     If out_dir is given, writes conflicts.json, report.md, and the intermediate
     artifacts (assertions.json, clusters.json, and one json per QAQC stage).
     """
@@ -70,7 +77,29 @@ def run_pipeline(
 
     reset_cost()
     set_text_backend(text_local)
+    set_model_overrides(vision_model, text_model)
+    if vision_model or text_model:
+        print(f"[Runner] model overrides: vision={vision_model or '(default)'} "
+              f"text={text_model or '(default)'}")
 
+    try:
+        return _run_stages(
+            pdf_path, out_dir, stage, project_input, source_name, text_local,
+        )
+    finally:
+        # Don't leak per-run picks into a later overlapping/CLI call.
+        set_model_overrides(None, None)
+        set_text_backend(False)
+
+
+def _run_stages(
+    pdf_path: str,
+    out_dir: Optional[str],
+    stage: Callable[[str], None],
+    project_input: Optional[Dict],
+    source_name: Optional[str],
+    text_local: bool,
+) -> Dict:
     stage("PDF -> images")
     pages = convert_pdf_to_images(pdf_path)
 

frontend/index.html

@@ -60,7 +60,15 @@
                  border:1px solid var(--line); background:#0c0e13; color:var(--text); font-size:16px; outline:none; }
   .email-card input[type=email]::placeholder { color:#6b7280; }
   .email-card input[type=email]:focus { border-color:var(--accent); box-shadow:0 0 0 3px rgba(91,140,255,.18); }
+  .email-card select { width:100%; padding:10px 12px; border-radius:8px; margin-top:6px;
+                       border:1px solid var(--line); background:#0c0e13; color:var(--text); font-size:14px; }
+  .email-card .field { margin-top:12px; }
+  .email-card .field > span { display:block; font-size:13px; color:var(--muted); margin-bottom:2px; }
   .btn.full { width:100%; padding:14px; font-size:15px; margin-top:0; }
+  .logbox { background:#0c0e13; border:1px solid var(--line); border-radius:8px; padding:12px 14px;
+            margin-top:10px; max-height:320px; overflow:auto; font:12px/1.45 ui-monospace,SFMono-Regular,Menlo,Consolas,monospace;
+            color:#c6cdd8; white-space:pre-wrap; word-break:break-word; }
+  .logbox .empty-log { color:var(--muted); }
   .note { background:var(--panel); border:1px solid var(--line); border-radius:10px;
           padding:16px 18px; margin:18px 0; }
   .note b { color:var(--text); }
@@ -121,12 +129,22 @@
     <label style="display:block;font-weight:400;margin-top:6px">
       <input type="radio" name="compute" value="local"> Hybrid &mdash; text stages on local LLM (cheaper, slower)</label>
     <div id="modelPick" style="margin-top:10px">
-      <label for="model" style="font-weight:400">Model <span class="opt" id="modelNote">loading...</span></label>
+      <div class="field">
-      <select id="model" style="width:100%;margin-top:6px;padding:10px;border-radius:8px;border:1px solid var(--line);background:#0c0e13;color:var(--text)"></select>
+        <span>Vision model <span class="opt">(image stages)</span> <span class="opt" id="modelNote">loading...</span></span>
+        <select id="vision_model" disabled><option value="">Loading models&hellip;</option></select>
+      </div>
+      <div class="field">
+        <span>Text model <span class="opt">(non-image stages)</span></span>
+        <select id="text_model" disabled><option value="">Loading models&hellip;</option></select>
+      </div>
     </div>
   </div>
   <button class="btn full" id="run" disabled>Run conflict check</button>
   <div class="status" id="status"></div>
+  <div id="liveLog" style="display:none" class="note">
+    <b>Run log</b> <span class="opt" id="logHint">(updates live)</span>
+    <pre class="logbox" id="logBox"><span class="empty-log">Waiting for output&hellip;</span></pre>
+  </div>
   <div id="results"></div>
 </main>
 <div id="viewer">
@@ -144,9 +162,71 @@
 const drop=document.getElementById('drop'), fileInput=document.getElementById('file'),
       runBtn=document.getElementById('run'), statusEl=document.getElementById('status'),
       results=document.getElementById('results'), dropLabel=document.getElementById('dropLabel'),
-      emailEl=document.getElementById('email');
+      emailEl=document.getElementById('email'),
+      visionSel=document.getElementById('vision_model'),
+      textSel=document.getElementById('text_model'),
+      liveLog=document.getElementById('liveLog'),
+      logBox=document.getElementById('logBox'),
+      logHint=document.getElementById('logHint');
 let chosen=null, polling=null, currentJobId=null, sheetPage={}, viewerZoom=1, reviewDirty=false;
 
+function modelLabel(m){
+  // Include per-1M-token pricing when the catalog provides it.
+  let s=m.name||m.id;
+  if(m.prompt_usd_per_mtok!=null)
+    s+=' — $'+m.prompt_usd_per_mtok+' / $'+m.completion_usd_per_mtok+' per 1M tok';
+  return s;
+}
+
+function fillSelect(sel, items, preferred){
+  sel.innerHTML='';
+  (items||[]).forEach(m=>{
+    const opt=document.createElement('option');
+    opt.value=m.id; opt.textContent=modelLabel(m);
+    if(m.id===preferred) opt.selected=true;
+    sel.appendChild(opt);
+  });
+  if(!sel.options.length){
+    const opt=document.createElement('option');
+    opt.value=preferred||''; opt.textContent=preferred||'(no models)';
+    sel.appendChild(opt);
+  }
+  sel.disabled=false;
+}
+
+let modelsLoaded=false;
+async function loadModels(){
+  const note=document.getElementById('modelNote');
+  try{
+    const res=await fetch('/models');
+    if(!res.ok) throw new Error('models HTTP '+res.status);
+    const data=await res.json();
+    const defs=data.defaults||{};
+    fillSelect(visionSel, data.vision, defs.vision);
+    fillSelect(textSel, data.text, defs.text);
+    modelsLoaded=true;
+    note.textContent='('+(data.text||[]).length+' text / '+(data.vision||[]).length+' vision available)';
+  }catch(err){
+    visionSel.innerHTML='<option value="">(default)</option>';
+    textSel.innerHTML='<option value="">(default)</option>';
+    visionSel.disabled=false; textSel.disabled=false;
+    note.textContent='using configured defaults (list unavailable)';
+    console.warn('Could not load models:', err);
+  }
+}
+
+function showLog(lines, live){
+  liveLog.style.display='block';
+  logHint.textContent=live?'(updates live)':'(saved with this job)';
+  const arr=lines||[];
+  if(!arr.length){
+    logBox.innerHTML='<span class="empty-log">No log lines yet&hellip;</span>';
+    return;
+  }
+  logBox.textContent=arr.join('\n');
+  logBox.scrollTop=logBox.scrollHeight;
+}
+
 function setFile(f){ chosen=f; dropLabel.textContent=f?('Selected: '+f.name):'Drop a PDF drawing set here, or click to choose';
                      runBtn.disabled=!f; }
 dropLabel.addEventListener('click',()=>fileInput.click());
@@ -160,6 +240,7 @@ runBtn.addEventListener('click',async e=>{
   if(!chosen) return;
   runBtn.disabled=true; results.innerHTML='';
   statusEl.innerHTML='<span class="spinner"></span>Uploading...';
+  showLog([], true);
   const fd=new FormData(); fd.append('file',chosen);
   const email=(emailEl.value||'').trim(); if(email) fd.append('notification_email',email);
   ['project_name','address','occupancy','work_type'].forEach(id=>{
@@ -168,8 +249,9 @@ runBtn.addEventListener('click',async e=>{
   const compute=(document.querySelector('input[name="compute"]:checked')||{}).value;
   fd.append('text_local', compute==='local' ? 'true' : 'false');
   if(compute==='openrouter'){
-    const modelSel=document.getElementById('model');
+    // Model picks only apply to OpenRouter compute; hybrid keeps its local model.
-    if(modelSel.value) fd.append('model', modelSel.value);
+    if(visionSel.value) fd.append('vision_model', visionSel.value);
+    if(textSel.value) fd.append('text_model', textSel.value);
   }
   const pipelineMode=(document.querySelector('input[name="pipeline_mode"]:checked')||{}).value||'classic';
   fd.append('pipeline_mode',pipelineMode);
@@ -196,21 +278,30 @@ function poll(jobId){
       const res=await fetch('/jobs/'+jobId);
       if(!res.ok) throw new Error('job not found');
       const job=await res.json();
+      const live=['running','queued','finalizing'].includes(job.status);
+      if(job.log_tail && job.log_tail.length) showLog(job.log_tail, live);
       if(job.status==='running'||job.status==='queued'){
         statusEl.innerHTML='<span class="spinner"></span>'+esc(job.stage||'Working...')+
           ' &middot; you can leave this page';
       } else if(job.status==='done'){
-        clearInterval(polling); polling=null; runBtn.disabled=false; render(job.report);
+        clearInterval(polling); polling=null; runBtn.disabled=false;
+        if(job.log && job.log.length) showLog(job.log, false);
+        render(job.report);
       } else if(job.status==='needs_review'||job.status==='reviewing'){
-        clearInterval(polling); polling=null; runBtn.disabled=false; renderReview(job);
+        clearInterval(polling); polling=null; runBtn.disabled=false;
+        if(job.log && job.log.length) showLog(job.log, false);
+        renderReview(job);
       } else if(job.status==='finalizing'){
         statusEl.innerHTML='<span class="spinner"></span>Finalizing reviewed report...';
       } else if(job.status==='finalization_error'){
         clearInterval(polling); polling=null; runBtn.disabled=false;
         statusEl.textContent='Finalization failed: '+(job.error||'unknown error');
+        if(job.log && job.log.length) showLog(job.log, false);
       } else if(job.status==='error'){
         clearInterval(polling); polling=null; runBtn.disabled=false;
         statusEl.textContent='Run failed: '+(job.error||'unknown error');
+        if(job.log && job.log.length) showLog(job.log, false);
+        else if(job.log_tail && job.log_tail.length) showLog(job.log_tail, false);
       }
     }catch(err){ clearInterval(polling); polling=null; runBtn.disabled=false;
                  statusEl.textContent='Error: '+err.message; }
@@ -230,29 +321,11 @@ function syncPipelineOptions(){
 document.querySelectorAll('input[name="pipeline_mode"]').forEach(el=>el.addEventListener('change',syncPipelineOptions));
 syncPipelineOptions();
 
-// --- model picker (OpenRouter compute only) ---
+// --- model pickers (OpenRouter compute only) ---
-let modelList=null;
-async function loadModels(){
-  const note=document.getElementById('modelNote'), sel=document.getElementById('model');
-  try{
-    const res=await fetch('/models');
-    if(!res.ok) throw new Error('list unavailable');
-    const data=await res.json();
-    modelList=data.models||[];
-    sel.innerHTML=modelList.map(m=>
-      '<option value="'+escAttr(m.id)+'"'+(m.id===data.default?' selected':'')+'>'+
-      esc(m.name||m.id)+' &mdash; $'+esc(m.prompt_usd_per_mtok)+' / $'+esc(m.completion_usd_per_mtok)+
-      ' per 1M tok</option>').join('');
-    note.textContent='('+modelList.length+' available)';
-  }catch(e){
-    sel.innerHTML='';
-    note.textContent='using configured default (list unavailable)';
-  }
-}
 function syncCompute(){
   const openrouter=(document.querySelector('input[name="compute"]:checked')||{}).value==='openrouter';
   document.getElementById('modelPick').style.display=openrouter?'block':'none';
-  if(openrouter&&!modelList) loadModels();
+  if(openrouter&&!modelsLoaded) loadModels();
 }
 document.querySelectorAll('input[name="compute"]').forEach(el=>el.addEventListener('change',syncCompute));
 syncCompute();
@@ -394,6 +467,10 @@ function render(rep){
     html+='</details>';
   }
 
+  html+='<div class="meta" style="margin-top:14px">Full run log: <a href="/jobs/'+
+    esc(currentJobId)+'/log?plain=1" target="_blank" rel="noopener">/jobs/'+
+    esc(currentJobId)+'/log</a> (also saved as job.log on the server)</div>';
+
   results.innerHTML=html;
 }
 function stat(v,l){ return '<div class="stat"><b>'+esc(v)+'</b><span>'+esc(l)+'</span></div>'; }

tests/api/test_job_log.py

@@ -60,18 +60,56 @@ def test_job_log_endpoint_serves_log_and_404s(job_env, monkeypatch):
     assert client.get("/jobs/nope/log").status_code == 404
 
 
-def test_model_override_set_and_cleared_around_run(job_env, monkeypatch):
+def test_model_overrides_passed_to_classic_runner(job_env, monkeypatch):
-    from backend import llm
+    """Classic mode: per-run picks travel as run_pipeline kwargs (the runner
-
+    sets and clears llm.set_model_overrides itself)."""
     seen = {}
 
     def fake_runner(pdf_path, **kwargs):
-        seen["override"] = llm._model_override
+        seen.update(kwargs)
         return {"source": "set.pdf", "summary": {}}
 
     monkeypatch.setattr("backend.jobs.run_pipeline", fake_runner)
     jobs.create_job(str(job_env / "set.pdf"), "set.pdf",
-                    pipeline_mode="classic", model="openai/gpt-4o")
+                    pipeline_mode="classic",
+                    vision_model="openai/gpt-4o", text_model="openai/gpt-4o-mini")
 
-    assert seen["override"] == "openai/gpt-4o"
+    assert seen["vision_model"] == "openai/gpt-4o"
-    assert llm._model_override is None  # cleared after the run
+    assert seen["text_model"] == "openai/gpt-4o-mini"
+
+
+def test_model_overrides_set_and_cleared_around_agent_run(job_env, monkeypatch):
+    """Agent mode: the agent runner has no override params, so jobs.py sets
+    them module-level for the duration of the run."""
+    from backend import llm
+
+    seen = {}
+
+    def fake_agent_runner(pdf_path, **kwargs):
+        seen["vision"] = llm._vision_model_override
+        seen["text"] = llm._text_model_override
+        return {"source": "set.pdf", "summary": {}}
+
+    monkeypatch.setattr("backend.jobs.run_agent_pipeline", fake_agent_runner)
+    jobs.create_job(str(job_env / "set.pdf"), "set.pdf",
+                    pipeline_mode="agent",
+                    vision_model="openai/gpt-4o", text_model="openai/gpt-4o-mini")
+
+    assert seen["vision"] == "openai/gpt-4o"
+    assert seen["text"] == "openai/gpt-4o-mini"
+    assert llm._vision_model_override is None  # cleared after the run
+    assert llm._text_model_override is None
+
+
+def test_failed_run_logs_traceback(job_env, monkeypatch):
+    """A crashed job must leave the traceback in job.log, not just str(e)."""
+    def boom(pdf_path, **kwargs):
+        raise RuntimeError("kaboom-stage-failure")
+
+    monkeypatch.setattr("backend.jobs.run_pipeline", boom)
+    job_id = jobs.create_job(str(job_env / "set.pdf"), "set.pdf", pipeline_mode="classic")
+
+    assert jobs._jobs[job_id]["status"] == "error"
+    content = (job_env / job_id / "job.log").read_text()
+    assert "Traceback (most recent call last)" in content
+    assert "RuntimeError: kaboom-stage-failure" in content

tests/api/test_models_api.py

@@ -11,12 +11,23 @@ _PAYLOAD = {
             "name": "GPT-4o",
             "pricing": {"prompt": "0.0000025", "completion": "0.00001"},
             "context_length": 128000,
+            "architecture": {"input_modalities": ["text", "image"],
+                             "output_modalities": ["text"]},
         },
         {
             "id": "google/gemini-2.5-pro",
             "name": "Gemini 2.5 Pro",
             "pricing": {"prompt": "0.00000125", "completion": "0.00001"},
             "context_length": 1000000,
+            "architecture": {"modality": "text+image->text"},
+        },
+        {
+            "id": "meta-llama/llama-3.1-70b-instruct",
+            "name": "Llama 3.1 70B Instruct",
+            "pricing": {"prompt": "0.0000005", "completion": "0.0000008"},
+            "context_length": 131072,
+            "architecture": {"input_modalities": ["text"],
+                             "output_modalities": ["text"]},
         },
     ]
 }
@@ -34,14 +45,27 @@ def test_models_endpoint_normalizes_pricing(monkeypatch):
     response = client.get("/models")
     assert response.status_code == 200
     body = response.json()
-    assert body["default"] == config.MODEL
+    assert body["defaults"] == {"vision": config.MODEL, "text": config.TEXT_MODEL}
-    assert body["default_text"] == config.TEXT_MODEL
+    by_id = {m["id"]: m for m in body["text"]}
-    by_id = {m["id"]: m for m in body["models"]}
     assert by_id["openai/gpt-4o"]["prompt_usd_per_mtok"] == 2.5
     assert by_id["openai/gpt-4o"]["completion_usd_per_mtok"] == 10.0
     assert by_id["openai/gpt-4o"]["context_length"] == 128000
 
 
+def test_models_endpoint_splits_vision_and_text(monkeypatch):
+    _reset_cache()
+    monkeypatch.setattr(models, "_fetch_openrouter_models", lambda: _PAYLOAD["data"])
+    client = TestClient(app)
+    body = client.get("/models").json()
+    vision_ids = {m["id"] for m in body["vision"]}
+    text_ids = {m["id"] for m in body["text"]}
+    # Both modality shapes (structured and legacy string) are recognized.
+    assert vision_ids == {"openai/gpt-4o", "google/gemini-2.5-pro"}
+    # Text list is the full catalog; vision models appear in both.
+    assert text_ids == {"openai/gpt-4o", "google/gemini-2.5-pro",
+                        "meta-llama/llama-3.1-70b-instruct"}
+
+
 def test_models_endpoint_caches(monkeypatch):
     _reset_cache()
     calls = []

tests/test_llm_model_override.py

@@ -1,26 +1,44 @@
 from backend import config
-from backend.llm import _resolve_backend, set_model_override
+from backend.llm import _resolve_backend, set_model_overrides, set_text_backend
 
 
-def test_override_wins_for_vision_and_text():
+def teardown_function():
-    set_model_override("openai/gpt-4o")
+    set_model_overrides(None, None)
-    try:
+    set_text_backend(False)
-        assert _resolve_backend(has_images=True, model_override=None)["model"] == "openai/gpt-4o"
+
-        assert _resolve_backend(has_images=False, model_override=None)["model"] == "openai/gpt-4o"
+
-    finally:
+def test_vision_override_wins_for_vision_only():
-        set_model_override(None)
+    set_model_overrides(vision="openai/gpt-4o", text=None)
+    assert _resolve_backend(has_images=True, model_override=None)["model"] == "openai/gpt-4o"
+    assert _resolve_backend(has_images=False, model_override=None)["model"] == config.TEXT_MODEL
+
+
+def test_text_override_wins_for_text_only():
+    set_model_overrides(vision=None, text="anthropic/claude-sonnet-4")
+    assert _resolve_backend(has_images=False, model_override=None)["model"] == "anthropic/claude-sonnet-4"
+    assert _resolve_backend(has_images=True, model_override=None)["model"] == config.MODEL
 
 
 def test_override_beats_per_call_model_arg():
-    set_model_override("openai/gpt-4o")
+    set_model_overrides(vision="openai/gpt-4o", text="openai/gpt-4o-mini")
-    try:
+    # Agents pass their AGENT_*_MODEL per call; the user's job pick wins.
-        # Agents pass their AGENT_*_MODEL per call; the user's job pick wins.
+    assert _resolve_backend(has_images=True, model_override="other/model")["model"] == "openai/gpt-4o"
-        assert _resolve_backend(has_images=False, model_override="other/model")["model"] == "openai/gpt-4o"
+    assert _resolve_backend(has_images=False, model_override="other/model")["model"] == "openai/gpt-4o-mini"
-    finally:
-        set_model_override(None)
 
 
 def test_no_override_keeps_defaults():
-    set_model_override(None)
+    set_model_overrides(None, None)
     assert _resolve_backend(has_images=True, model_override=None)["model"] == config.MODEL
     assert _resolve_backend(has_images=False, model_override=None)["model"] == config.TEXT_MODEL
+
+
+def test_ui_picks_never_name_the_local_model(monkeypatch):
+    """Hybrid runs keep LOCAL_TEXT_MODEL; OpenRouter picks must not leak into
+    the local endpoint (a vLLM server won't serve OpenRouter model ids)."""
+    monkeypatch.setattr(config, "LOCAL_BASE_URL", "http://localhost:8000/v1")
+    monkeypatch.setattr(config, "LOCAL_TEXT_MODEL", "qwen/local-instruct")
+    set_text_backend(True)
+    set_model_overrides(vision="openai/gpt-4o", text="anthropic/claude-sonnet-4")
+    be = _resolve_backend(has_images=False, model_override=None)
+    assert be["local"] is True
+    assert be["model"] == "qwen/local-instruct"